Methods for Extracting Fractional Vegetation Cover from Differentiated Scenarios Based on Unmanned Aerial Vehicle Imagery DOI Creative Commons
Chih‐Hong Sun, Yonggang Ma, Heng Pan

и другие.

Land, Год журнала: 2024, Номер 13(11), С. 1840 - 1840

Опубликована: Ноя. 5, 2024

Fractional vegetation cover (FVC) plays a key role in ecological and environmental status assessment because it directly reflects the extent of its status, yet is an important component ecosystems. FVC estimation methods have evolved from traditional manual interpretation to advanced remote sensing technologies, such as satellite data analysis unmanned aerial vehicle (UAV) image processing. Extraction based on high-resolution UAV are being increasingly studied fields ecology sensing. However, research UAV-based extraction against backdrop high soil reflectance arid regions remains scarce. In this paper, 12 visible light images differentiated scenarios Ebinur Lake basin, Xinjiang, China, various used for high-precision estimation: Otsu’s thresholding method combined with Visible Vegetation Indices (abbreviated Otsu-VVIs) (excess green index, excess red minus normalized green–red difference green–blue red–green ratio color index extraction, visible-band-modified soil-adjusted modified red–green–blue visible-band index), space (red, green, blue, hue, saturation, value, lightness, ‘a’ (Green–Red component), ‘b’ (Blue–Yellow component)), linear mixing model (LMM), two machine learning algorithms (a support vector neural network). The results show that following exhibit accuracy across scenarios: Otsu–CIVE, (‘a’: Green–Red LMM, SVM (Accuracy > 0.75, Precision 0.8, kappa coefficient 0.6). Nonetheless, higher scene complexity entropy reduce applicability precise methods. This study facilitates accurate, efficient information within semiarid regions, providing technical references similar areas.

Язык: Английский

Runoff evolution characteristics and its response to climate change in the middle and lower reaches of Shule River Basin, Northwest China DOI

Dongyuan Sun,

Yiru Wang, Lanzhen Wu

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102436 - 102436

Опубликована: Май 5, 2025

Язык: Английский

Процитировано

0
Time-lag and accumulation responses of vegetation to precipitation in the Jinsha River dry-hot valley at multiple spatial–temporal scales DOI
Jiancan Feng, Guokun Chen, Xingwu Duan

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133605 - 133605

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Historical and projected extreme climate changes in the upper Yellow River Basin, China DOI Creative Commons
Shihao Chen, Baohui Men,

Jinfeng Pang

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 30, 2025

Considering plateau climate and complex terrain of the upper Yellow River Basin, understanding changes in extremes has become increasingly urgent. This study highlighted historical from 1960 to 2022 based on 20 extreme indices, future until 2100 under two Shared Socioeconomic Pathways (SSP126 SSP585) Coupled Model Intercomparison Project phase 6 (CMIP6) models. We found that spatial temporal evolutions precipitation (PEs) temperature (TEs) primarily exhibit increasing trends. The frequency intensity PEs show an trend, while duration shows a decreasing trend. Both cold extremes, as well intensity, frequency, warm Future TEs are expected continue intensify even most ideal scenario (i.e., SSP126), these anticipated further with radiative forcing levels greenhouse gas concentrations. Results could provide scientific references for better coping regions scarce observation station.

Язык: Английский

Процитировано

0
Spatiotemporal Characteristics and Prediction of Ecological Safety in the Yellow River Basin of China DOI Creative Commons
Xiao Wei, Lifeng Zhang, Yi He

и другие.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 16119 - 16138

Опубликована: Янв. 1, 2024

s-The Yellow River Basin (YRB) is a major ecological functional area in China, and its safety development change have extremely significant impacts on the natural environment human society. However, existing studies YRB lack spatiotemporal characteristics analysis prediction of with vegetation as core. Therefore, this study proposes to construct an index (ESI) based comprehensive multi-dimensional evaluation system "vigor-pressure-state-response,"using normalized difference index, carbon sink indicator parameters, temperature, precipitation, digital elevation model, population density, per capita gross domestic product from 2000 2020. The ESI were then analyzed for YRB, long-term short-term memory network model was constructed predict trend over next 10 years. According results, 2020, showed fluctuating upward trend, annual average changed abruptly 2015 due drastic changes hazardous areas. most areas stability weak some areas, overall spatial distribution positive agglomeration characteristics. Further, response landscape complexity different reaches varied. Most middle positively correlated complexity, while upper lower not significantly or negatively correlated. Notably, years, YRB's growth will slow down, degradation increasing, decreasing, currently showing improving.

Язык: Английский

Процитировано

2
Spatiotemporal Variation in Extreme Climate in the Yellow River Basin and its Impacts on Vegetation Coverage DOI Open Access
Zichuang Li, Huazhu Xue, Guotao Dong

и другие.

Forests, Год журнала: 2024, Номер 15(2), С. 307 - 307

Опубликована: Фев. 6, 2024

Global warming and extreme climate events (ECEs) have grown more frequent, it is essential to investigate the influences of ECEs on vegetation in Yellow River Basin (YRB) other environmentally fragile areas. This study was based data from 86 meteorological stations YRB for period 2000–2020. Twenty-five indices (ECIs) were chosen, encompassing four dimensions: value, intensity, duration, frequency. The trend analysis approach used examine spatiotemporal characteristics conditions. Additionally, geographical detectors Pearson correlation methods employed quantitatively assess influence Normalized Difference Vegetation Index (NDVI). Multiscale Geographically Weighted Regression (MGWR) method adopted analyze regression twenty-five ECIs. findings revealed following: (1) Over last 21 years, there has been a distinct rise both precipitation (EPIs) temperature (ETIs). (2) spatial distribution NDVI throughout year displayed characteristic being high south low north. annual demonstrated noteworthy increase at rate 0.055/decade, with enhancement an extensive area 87.33%. (3) investigation that EPIs, including PRCPTOT, R10mm, CWD, R95p, CDD, had explanatory values surpassing 0.4. implied frequency, duration played pivotal roles steering alterations YRB. (4) between EPIs greater than ETIs. Grassland meadows exhibited sensitivity woody plants. (excluding CDD SDII) ETIs (TXn) substantial positive regions hosting grasslands, broadleaf forests, shrubs. Desert cultivated plants less affected by ECEs. underscores importance interplay provides scientific basis formulating environmental safeguarding strategies.

Язык: Английский

Процитировано

1
Vegetation Dynamics and Driving Mechanisms Considering Time-Lag and Accumulation Effects: A Case Study of Hubao–Egyu Urban Agglomeration DOI Creative Commons
Xi Liu, Guoming Du,

Xiaodie Zhang

и другие.

Land, Год журнала: 2024, Номер 13(9), С. 1337 - 1337

Опубликована: Авг. 23, 2024

The Hubao–Egyu Urban Agglomeration (HBEY) was a crucial ecological barrier in northern China. To accurately assess the impact of climate change on vegetation growth, it is essential to consider effects time lag and accumulation. In this study, we used newly proposed kernel Normalized Difference Vegetation Index (kNDVI) as metric for condition, employed partial correlation analysis ascertain accumulation period response by considering different scenarios (No/Lag/Acc/LagAcc) various combinations. Moreover, further modified traditional residual model. results are follows: (1) From 2000 2022, HBEY experienced extensive persistent greening, with kNDVI slope 0.0163/decade. Precipitation identified dominant climatic factor influencing dynamics. (2) HBEY, effect temperature most distinct, particularly affecting cropland grassland. precipitation pronounced (3) Incorporating into models increases explanatory power impacts dynamics 6.95% compared models. Our findings hold implications regional regulation research.

Язык: Английский

Процитировано

1
Influences of climatic variation and human activities on vegetation photosynthesis dynamics in Southwest China DOI

Jingxuan Su,

Liangxin Fan,

Zhanliang Yuan

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122879 - 122879

Опубликована: Окт. 11, 2024

Язык: Английский

Процитировано

1
Differences in the Effects of Three Water Elements on Vegetation in Different Climatic Regions: Insights From the Yellow River Basin, China DOI Open Access
Xiaohui Jin,

Yumiao Fan,

Yawei Hu

и другие.

Ecohydrology, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 4, 2024

ABSTRACT The effects of water on vegetation have always been a concern. It is an important support as well major limiting factor with respect to growth. By analysing the spatiotemporal changes and correlations between precipitation (PRE), soil moisture (SM), vapour pressure deficit (VPD) normalized difference index (NDVI) in Yellow River Basin, we explored different three elements climatic regions. Our findings reveal following: (1) NDVI report increasing trend most significantly. 92.1% Basin showed increase NDVI. (2) Vegetation was positively affected by PRE, followed SM VPD. PRE mainly natural both sides boundary arid semi‐arid regions semi‐humid regions, whereas VPD crops irrigation areas, areas were most. These contribute deeper understanding relationship vegetation.

Язык: Английский

Процитировано

1
Analysis of Spatiotemporal Variation Characteristics and Influencing Factors of Grassland Vegetation Coverage in the Qinghai–Tibet Plateau from 2000 to 2023 Based on MODIS Data DOI Creative Commons

Shi Xing-he,

Dong Yang,

Shijian Zhou

и другие.

Land, Год журнала: 2024, Номер 13(12), С. 2127 - 2127

Опубликована: Дек. 7, 2024

Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics Tibetan Plateau ecosystem, variations crucial its ecological stability. This study utilizes Google Earth Engine (GEE) platform retrieve long-term MODIS data and analyzes spatiotemporal distribution FVC across Qinghai–Tibet (QTP) over 24 years (2000–2023). The growth index (GI) is used evaluate annual at pixel level. GI an indicator for measuring status, which can effectively measure changes each year relative base year. trends monitored using Sen-Mann-Kendall slope estimation, coefficient variation, Hurst exponent. Geographic detectors partial correlation analysis then applied explore contribution rates key driving factors FVC. results show: (1) From 2000 2023, exhibited overall upward trend, with rate 0.0881%. on QTP follows a pattern higher values east lower west; (2) Over past years, 54.05% total area has shown significant increase, 23.88% remained stable, only small portion decrease. trend expected continue minimal variability, covering 82.36% area. suggests balanced state growth; (3) precipitation (Pre) soil moisture (SM) main single affecting grasslands (q = 0.59 0.46). In interaction detection, addition highest between Pre other factors, SM also showed impact grassland; hydrothermal grassland. It shows that stronger than temperature. enhanced our understanding change quantitatively described relationship great significance maintaining sustainable development ecosystems.

Язык: Английский

Процитировано

1
Phenological Horizon Attention Transformer (Phat) Crop Mapping Method Using Modis Time-Series Imagery: A Case Study in the North China Plain DOI

Quanshan Gao,

Taixia Wu, Jingyu Yang

и другие.

Опубликована: Янв. 1, 2024

Accurate crop planting data collecting is essential for achieving sustainable development goals like estimating agricultural productivity and ensuring food security. The simultaneous high-precision extraction of various crops a challenging task due to the fragmentation cultivated land phenological variations in vast region. Even yet, time series information becomes an effective feature method through analyzing differences. However, latitude differences large regions, even same planting, maturation other crucial periods are inconsistent, resulting results extracted from data. This paper developed advanced deep learning method, i.e., horizon attention mechanism-Transformer (PHAT). using normalized differential vegetation index (NDVI) dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) product construct range Considering crops, orthogonal subspace projection (OSP) vertex component analysis (VCA) were used determine type extract curves. Meanwhile, regular change NDVI revealed evolution trend among multiple but characteristics difference between extremely difficult find. Therefore, PHAT model was solve problem curve Afterward, we verified accuracy algorithm Google Earth Landsat 8 images. Based MODIS with 250m coarse spatial resolution, overall our synchronous five 90.1%, root mean square error (RMSE) approximately 12%, which satisfactory result.

Язык: Английский

Процитировано

0